Optimal route/path selection in Software Defined Networks, including Software Defined Wide Area Networks (SD-WAN), may be based on network performance metrics, such as delay, jitter, packet loss ratios, bandwidth, and others. Systems for providing route selection rely on performance management probes and local Key Performance Indicators (KPIs) to make path selection decisions based on a preset policy that compares Service Level Agreement (SLA) data and KPIs against predefined thresholds and watermarks.
Machine learning provides an alternate approach for optimal route selection. Machine learning optimal route selection involves building a predictive model for the routing logic that is capable of optimizing the path selection process without relying on complex policies and pre-configured thresholds. The machine learning logic monitors the performance metrics and local KPIs to refine its predictions as it learns.